1,632 responses from 544 respondents to 3 items.
One of 6 tables from the same source; this one is the purchase_intention table. The others are listed under Related tables.
| Description | 3-item Purchase Intention subscale (1-5 Likert), same survey as liu_2023_brand_trust |
|---|---|
| Reference | Liu Q, Wang X (2023) The impact of brand trust on consumers' behavior toward agricultural products' regional public brand. PLoS ONE. |
| DOI | 10.1371/journal.pone.0295133 |
| Licence | CC BY 4.0 |
| Source data | https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0295133.s001 |
| Responses | 1,632 |
|---|---|
| Respondents | 544 |
| Items | 3 |
| Response categories | 5 |
| Responses per respondent | 3 |
| Responses per item | 544 |
| Density | 1 |
| Longitudinal | FALSE |
| measurement tool | Survey/questionnaire |
|---|---|
| item format | Likert Scale/selected response |
| primary language(s) | chi |
This table has item text in the IRW: the wording administered to respondents, not just the response codes.
| Instrument | Purchase Intention toward agricultural products' regional public brand (3 items adapted from Sun et al. 2022 and Ajzen 2020) |
|---|---|
| Mean words per item | 1 |
| Mean characters per item | 2 |
| Mean characters per response | 7.400 |
| Flesch-Kincaid grade level | -3.400 |
This codebook is reconstructed by the IRW from the IRW data standard and the script that built it, liu_2023_brand_trust.py. It is our best reading, not the source’s own codebook, and it may contain mistakes. Where the two disagree, the source data are the authority. If you find an error, please tell us.
| Column | What it is | Source column |
|---|---|---|
| IRW standard columns | ||
id | IRW standard Identifier for the focal unit being measured — typically a person, but sometimes another entity (e.g., a word in a lexical task). | NO. |
item | IRW standard Item identifier. | made in the build script |
resp | IRW standard Response value. | made in the build script |
What item codes and covariate values stand for is in the codebook released with the source data.
Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("liu_2023_purchase_intention")
# Python
pip install irw
import irw
df = irw.fetch("liu_2023_purchase_intention")
@article{Liu_2023, title={The impact of brand trust on consumers’ behavior toward agricultural products’ regional public brand}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0295133}, DOI={10.1371/journal.pone.0295133}, number={11}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Liu, Qiyun and Wang, Xinyuan}, editor={Trzebiński, Wojciech}, year={2023}, month=Nov, pages={e0295133} }
| IRW version | v549 |
|---|---|
| Redivis dataset | item_response_warehouse_3 v13.0 |
| Redivis dataset DOI | 10.57761/gpg7-sg87 |
| Manifest pin for this IRW version | v13.0 |
| Metadata source | irw_meta v39.0 |